6th International Workshop on
Science, technology, and commerce increasingly recognise the importance of machine learning approaches for data-intensive, evidence-based decision making. Moreover, the number of machine learning applications and the volumes of data increase permanently. Nevertheless, the capacities of processing systems, human supervisors, or domain experts remain limited in real-world applications. Furthermore, many applications require a fast reaction to new situations, which means that first predictive models need to be available even if little data is yet available. Therefore, approaches that optimise the whole learning process are needed, including the interaction with human supervisors, processing systems, and data of various kind and at different timings: techniques for estimating the impact of additional resources (e.g. data) on the learning progress; methods for active selection of the information processed or queried; techniques for reusing knowledge across time, domains, or tasks, by identifying similarities and adaptation to changes between them; methods for making use of different types of information, such as labelled or unlabelled data, constraints, or domain knowledge. Such techniques are studied, for example, in the fields of adaptive, active, semi-supervised, and transfer learning -- mostly in separate lines of research. Combinations that are capable of operating under various constraints, and thereby address the inherent real-world challenges of volume, velocity, and variability of data and data mining systems, are rarely reported. Therefore, this workshop aims to bring together researchers and practitioners from these different areas, and to stimulate research in interactive and adaptive machine learning systems as a whole. It continues a successful series of events at ECML PKDD 2017 in Skopje (Workshop and Tutorial), IJCNN 2018 in Rio (Tutorial), ECML PKDD 2018 in Dublin (Workshop), ECML PKDD 2019 in Würzburg (Workshop and Tutorial), and ECML PKDD 2020 (hosted in Ghent, online Workshop).
The workshop aims at discussing techniques and approaches for optimising the whole learning process, including the interaction with human supervisors, processing systems, and includes adaptive, active, semi-supervised, and transfer learning techniques, and combinations thereof in interactive and adaptive machine learning systems make the topic of this workshop. Our objective is to bridge the communities researching and developing these techniques and systems in machine learning and data mining. Therefore, we welcome contributions that present a new problem setting, propose a novel approach, or report experience with the practical deployment of such a system and raise unsolved questions to the research community.
In particular, we welcome contributions that address aspects including, but not limited to:
The full paper track covers new innovative contributions in the area of interactive adaptive learning. If you have a new method already evaluated briefly, a new tool to simplify interaction or some new insights the community might benefit from, please submit a regular paper. The page limit is 8-16 pages (excluding references).
Submission Deadline: 27 June 2022
The extended abstract track is ideal to discuss new ideas in the area of interactive adaptive learning. We encourage you to submit open challenges in research or industrial applications to initiate a discussion and find colleagues to collaborate with. The page limit is 2-4 pages (excluding references).
Submission Deadline: 27 June 2022
All accepted papers will be published at ceur-ws.org (indexed by e.g. google scholar) or within Springer LNCS proceedings depending on the number of submissions. Reviews are single-blind.
The paper must be be written in English and contain author names, affiliations, and email addresses. The paper must be in PDF using the LNCS format. See instructions here.
All accepted papers are presented in spotlight talks and/or poster sessions. At least one author of each accepted paper must be registered to the workshop.
Time | Program | Presenter/Author |
---|---|---|
14:30 - 16:30 | Session 1: | |
5m | Introduction | |
60m | Invited Talk: Title: Active meta-learning | Isabelle Guyon |
15m | A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning | M. Herde, D. Huseljic, J. Mitrović, M. Granitzer, B. Sick |
20m | BioSegment: Active Learning segmentation for 3D imaging | B. Rombaut, J. Roels, Y. Saeys |
20m | Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources | L. Rauch, D. Huseljic, B. Sick |
Break | ||
17:00 - 18:05 | Session 2: | |
15m | Accelerating Diversity Sampling for Deep Active Learning By Low-Dimensional Representations | S. Gilhuber, M. Berrendorf, Y. Ma, T. Seidl |
20m | A Practical Evaluation of Active Learning Approaches for Object Detection | J. Schneegans, M. Bieshaar, B. Sick |
20m | Certifiable Active Class Selection in Multi-Class Classification | M. Senz, M. Bunse, K. Morik |
10m | Closing and information about scikit-activeml | D. Kottke |
daniel.kottke (at) uni-kassel.de
University of Kassel, Germany
g.m.krempl (at) uu.nl
Utrecht University, Netherlands
bhammer (at) techfak.uni-bielefeld.de
University of Bielefeld, Germany
a.holzinger (at) hci-kdd.org
University of Natural Resources and Life Sciences Vienna, Austria
vincent.lemaire (at) orange.com
Orange Labs, France
polikar (at) rowan.edu
Rowan University, USA
bsick (at) uni-kassel.de
University of Kassel, Germany
adrian.calma (at) uni-kassel.de
University of Kassel, Germany